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Development Methodologies for Big Data Analytics Systems

Overview of attention for book
Development Methodologies for Big Data Analytics Systems
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Open Source IT for Delivering Big Data Analytics Systems as Services: A Selective Review
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    Chapter 2 The Role of Machine Learning in Big Data Analytics: Current Practices and Challenges
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    Chapter 3 The Data Value Chain Ontology
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    Chapter 4 Requirements for Machine Learning Methodology Software Tooling
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    Chapter 5 A Selective Conceptual Review of CRISP-DM and DDSL Development Methodologies for Big Data Analytics Systems
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    Chapter 6 A Selective Comparative Review of CRISP-DM and TDSP Development Methodologies for Big Data Analytics Systems
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    Chapter 7 BDAS-EPM: An Integrated Evolution Process Model for Big Data Analytics Systems
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    Chapter 8 Big Data Adoption Factors and Development Methodologies: A Multiple Case Study Analysis
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    Chapter 9 Detection of Breast Cancer in Mammography Using Pretrained Convolutional Neural Networks with Fine-Tuning
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    Chapter 10 Challenges and Opportunities of Intercompany Big Data Analytics in Supply Chains
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    Chapter 11 From Big Data to Big Insights: A Synthesis of Real-World Applications of Big Data Analytics
Attention for Chapter 4: Requirements for Machine Learning Methodology Software Tooling
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